In this study, UAV multispectral imagery is used to segment the severity of bacterial leaf blight (BLB) in rice using convolutional neural networks (CNNs) and transformer-based models. The evaluated architectures include U-Net with a ResNet- 101 encoder, U-Net++ with EfficientNet-B3 and EfficientNetB7, DeepLabV3+, and SegFormer, all trained under a common pipeline with three input configurations (multispectral only, multispectral+NDVI, and multispectral+NDRE). Experiments are conducted using the publicly available BLB dataset with performance reported using mean IoU (mIoU), mean F1 (mF1), mean accuracy (mAcc), precision, and recall. U-Net++ with EfficientNet-B3 achieved the highest performance, with an mIoU of 97.62%. SegFormer obtained lower segmentation accuracy but comparable inference speed. Overall, the results indicate that lightweight CNN backbones remain more reliable for operational BLB monitoring while integration of vegetation indices provides small and consistent improvements. The study also highlights the value of standardised UAV datasets to compare disease mapping methods and encourages the use of CNN architectures for field implementation.
基于无人机多光谱影像的水稻病害制图深度学习框架比较 / Comparison of Deep Learning Frameworks For Rice Disease Mapping From UAV Multispectral Imaging
本研究通过对比多种深度学习模型（如U-Net、SegFormer等）在无人机多光谱影像上识别水稻细菌性叶枯病严重程度的表现，发现基于轻量级CNN骨干网络的U-Net++（搭配EfficientNet-B3）在准确率上最优（平均交并比达97.62%），且比复杂Transformer模型更适合实际监测应用。
源自 arXiv: 2606.06359
